Rapid projections for B-ALL data

library(Seurat)
Warning messages:
1: package ‘SeuratObject’ was built under R version 4.1.2 
2: package ‘sp’ was built under R version 4.1.2 
library(symphony)
library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.4.1     ✔ purrr   0.3.4
✔ tibble  3.1.2     ✔ dplyr   1.0.7
✔ tidyr   1.1.3     ✔ stringr 1.4.0
✔ readr   1.4.0     ✔ forcats 0.5.1
Warning: package ‘ggplot2’ was built under R version 4.1.2── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

Bone Marrow Reference Map

BM_projection_path = '../../../../../../AMLhierarchies/scRNA_projection/Complete_Hematopoiesis/BM_V2only/Final_Embedding/Final_projections/ProjectionTools/'
source(paste0(BM_projection_path, 'Symphony_Utils_BMref.R'))
BM_ref <- readRDS(paste0(BM_projection_path, 'BoneMarrow_RefMap_SymphonyRef.rds'))

# Fix uwot path for UMAP projection - point it to the directory with Projection Tools
BM_ref$save_uwot_path <- paste0(BM_projection_path, BM_ref$save_uwot_path)
ReferenceSeuratObj_BM <- CreateReferenceSeuratObj(BM_ref)
Warning: No assay specified, setting assay as RNA by default.Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from umap to umap_Warning: All keys should be one or more alphanumeric characters followed by an underscore '_', setting key to umap_
DimPlot(ReferenceSeuratObj_BM, reduction = 'umap', group.by = 'CellType_Annotation_formatted', raster=FALSE, label=TRUE, label.size = 4)

Cross-Ontogeny B-Development Map

Bdev_projection_path = 'BDevelopment_ProjectionTools/'
Bdev_ref <- readRDS(paste0(Bdev_projection_path, 'BDevelopment_RefMap_SymphonyRef.rds'))

# Fix uwot path for UMAP projection - point it to the directory with Projection Tools
Bdev_ref$save_uwot_path <- paste0(Bdev_projection_path, Bdev_ref$save_uwot_path)

Run Samples

library(ggpointdensity)
library(viridis)
library(jcolors)
map_sample_BM <- function(seurat_obj, batch_key){
  
  # Map cells
  seurat_obj <- mapQuery(
      seurat_obj@assays$RNA@counts, 
      seurat_obj@meta.data,
      BM_ref,
      vars = batch_key, 
      return_type = 'Seurat'
  ) %>% calcMappingError(., reference = BM_ref, MAD.threshold = 2)
  
  # Predict Labels 
  seurat_obj <- knnPredict.Seurat(
    seurat_obj, 
    BM_ref, 
    label_transfer = 'CellType_Annotation', 
    k = 30
  ) 
  
  return(seurat_obj)
}
library(ggpointdensity)
library(jcolors)

plot_projection <- function(dat, ptx_id, refUMAP = refUMAP, downsample_reference = TRUE, save_folder = 'BALL_projections/Complete_BoneMarrowReference/Figures/'){
  
  if(downsample_reference){
    refUMAP <- refUMAP %>% sample_frac(0.25)
  }
  
  dat <- dat@meta.data %>% rownames_to_column('Cell') %>% 
    left_join(dat@reductions$umap@cell.embeddings %>% data.frame() %>% rownames_to_column('Cell') ) %>% 
    filter(mapping_error_QC == 'Pass') %>% 
    mutate(ref_query = 'query') %>% 
    bind_rows(refUMAP)
  
  ## Get the Background
  background <-  dat %>% filter(ref_query == 'reference') %>% select(-Sample)
  heatpalette <- heat.colors(12)
  
  p <- dat %>% 
    filter(ref_query == 'query') %>% 
    ggplot(aes(x = umap_1, y = umap_2)) +  
    geom_point(data = background, color='#E3E3E3', size=0.01, alpha=0.5) + 
    geom_pointdensity(size=0.05) +
    scale_color_jcolors_contin("pal3", reverse = TRUE, bias = 1.75) +
    geom_density_2d(alpha=0.4, color='black', h = 1.5, size=0.4) + 
    theme_void() + ggtitle(ptx_id) + 
    theme(strip.text.x = element_text(size=18), legend.position='none') 
  
  ggsave(paste0(save_folder, ptx_id, '_BoneMarrowReference_projectedUMAP.pdf'), height = 4, width = 6)
}
get_composition_BM <- function(BALL, composition_BM){
  # get composition per cell
  sample_composition <- BALL@meta.data %>% rownames_to_column('barcode') %>% 
    select(barcode, Sample, mapping_error_score, mapping_error_QC, 
           CellType_Annotation, CellType_Annotation_prob)
  
  # concatenate lol
  composition_BM <- bind_rows(composition_BM, sample_composition)
  return(composition_BM)
}

Now lets iterate through samples and run

basepath = 'BALL_rawdata/Expanded/'
matrixpath = '/filtered_feature_bc_matrix/'
BALL_patients <- list.files(basepath)
BALL_patients
 [1] "SJALL040053_D1"   "SJALL040066_D1"   "SJALL040069_D1"   "SJALL040070_D1"   "SJALL040099_D1"   "SJALL040100_D1"   "SJALL040103_D1"   "SJALL040119_D1"  
 [9] "SJALL040137"      "SJALL040137_D1"   "SJBALL004097_D2"  "SJBALL006_D"      "SJBALL006_R"      "SJBALL014876_D1"  "SJBALL021901_D1"  "SJBALL021947_D1" 
[17] "SJBALL021964_D1"  "SJBALL021968_D1"  "SJBALL021973_D1"  "SJBALL022020_D1"  "SJBALL022035_D1"  "SJBALL022052_D1"  "SJBALL030036_D1"  "SJBALL030040_D1" 
[25] "SJBALL030059_D1"  "SJBALL030072_D1"  "SJBALL030090_D1"  "SJBALL030095_D1"  "SJBALL030123_D1"  "SJBALL030123_R1"  "SJBALL030127_D1"  "SJBALL030145_D1" 
[33] "SJBALL030216_D1"  "SJBALL030247_D1"  "SJBALL030254_D1"  "SJBALL030276_D1"  "SJBALL030285_D1"  "SJBALL030313_D1"  "SJBALL030344_D1"  "SJBALL030370_D1" 
[41] "SJBALL030379_D1"  "SJBALL030414_D1"  "SJBALL030434_D1"  "SJBALL030491_D1"  "SJBALL030662_D1"  "SJBALL030718_D1"  "SJBALL030734_D1"  "SJBALL030762_D1" 
[49] "SJBALL030821_D1"  "SJBALL030871_D1"  "SJBALL030923_D1"  "SJBALL030971_D1"  "SJBALL030975_D1"  "SJBALL031052_D1"  "SJBALL031087_D1"  "SJBALL031128_D1" 
[57] "SJBALL031144_D1"  "SJBALL031168_D1"  "SJBALL031267_D1"  "SJBALL031281_D1"  "SJBALL081_D"      "SJBALL087_D"      "SJBALL104_D"      "SJBALL113_D"     
[65] "SJBALL182_D"      "SJBALL182_R"      "SJBALL211_D"      "SJBALL243_D"      "SJBALL255_D"      "SJE2A063_D"       "SJE2A066_D"       "SJE2A067_D"      
[73] "SJHYPER022017_D1" "SJHYPO021982_D1"  "SJHYPO117_D"      "SJHYPO120_D"      "SJHYPO124_D"      "SJHYPO143_D"      "SJHYPO146_D"      "SJINF022043_D"   
[81] "SJMLL006_D"       "SJMLL009_D"       "SJPHALL004_D"     "SJPHALL006_D"     "SJPHALL007_D"     "SJPHALL010_D"     "SJPHALL020_D"     "SJPHALL020_R"    
[89] "SJPHALL021_D"    
refUMAP <- data.frame(BM_ref$umap$embedding) %>% rename(umap_1 = X1, umap_2 = X2) %>% mutate(ref_query = 'reference')
BALL_composition_BMref <- data.frame()

# Test the first sample
for(pt_samp in BALL_patients){
  print(paste0('Mapping: ', pt_samp))
  start_time <- Sys.time()
  
  # load in as seurat obj
  BALL <- Seurat::Read10X(paste0(basepath, pt_samp, matrixpath)) %>% CreateSeuratObject()
  BALL$Sample <- pt_samp

  # QC >500 genes, >2500 counts, <8% mt counts
  BALL <- Seurat::PercentageFeatureSet(BALL, pattern = '^MT-', col.name = 'pct.mito')
  BALL <- subset(BALL, nFeature_RNA > 500 & nCount_RNA > 2500 & pct.mito < 8)
  print(paste0('Post QC Cells: n = ', dim(BALL)[2]))

  # Map sample onto reference map and label with 30 KNN, no batch correction
  BALL <- map_sample_BM(BALL, NULL)
  # plot projected cells onto UMAP 
  plot_projection(BALL, pt_samp, refUMAP = refUMAP, downsample_reference = TRUE, save_folder = 'BALL_projections/Complete_BoneMarrowReference/Figures/')
  # get celltype composition
  BALL_composition_BMref <- get_composition_BM(BALL, BALL_composition_BMref)
  # save
  BALL %>% saveRDS(paste0("BALL_projections/Complete_BoneMarrowReference/", pt_samp, "_projected.rds"))
  rm(BALL)
  
  end_time <- Sys.time()
  print(end_time - start_time)
  gc()
}
[1] "Mapping: SJALL040053_D1"
[1] "Post QC Cells: n = 4088"
Normalizing
Scaling and synchronizing query gene expression
Found 2360 reference variable genes in query dataset
Project query cells using reference gene loadings
Clustering query cells to reference centroids
Correcting query batch effects
UMAP
All done!
Warning: Invalid name supplied, making object name syntactically valid. New object name is Seurat..ProjectDim.SymphonyQuery.harmony; see ?make.names for more details on syntax validityJoining, by = "Cell"
Time difference of 1.814683 mins
[1] "Mapping: SJALL040066_D1"
[1] "Post QC Cells: n = 6880"
Time difference of 3.015293 mins
[1] "Mapping: SJALL040069_D1"
[1] "Post QC Cells: n = 8056"
Time difference of 3.585634 mins
[1] "Mapping: SJALL040070_D1"
[1] "Post QC Cells: n = 4486"
Time difference of 1.983575 mins
[1] "Mapping: SJALL040099_D1"
[1] "Post QC Cells: n = 5403"
Time difference of 2.30194 mins
[1] "Mapping: SJALL040100_D1"
[1] "Post QC Cells: n = 6897"
Time difference of 3.018985 mins
[1] "Mapping: SJALL040103_D1"
[1] "Post QC Cells: n = 4853"
Time difference of 2.030512 mins
[1] "Mapping: SJALL040119_D1"
[1] "Post QC Cells: n = 9313"
Time difference of 4.623792 mins
[1] "Mapping: SJALL040137"
[1] "Post QC Cells: n = 6202"
Time difference of 2.809618 mins
[1] "Mapping: SJALL040137_D1"
[1] "Post QC Cells: n = 6737"
Time difference of 2.995615 mins
[1] "Mapping: SJBALL004097_D2"
[1] "Post QC Cells: n = 2978"
Time difference of 1.398087 mins
[1] "Mapping: SJBALL006_D"
[1] "Post QC Cells: n = 7649"
Time difference of 3.345602 mins
[1] "Mapping: SJBALL006_R"
[1] "Post QC Cells: n = 4738"
Time difference of 2.025418 mins
[1] "Mapping: SJBALL014876_D1"
[1] "Post QC Cells: n = 1729"
Time difference of 52.1443 secs
[1] "Mapping: SJBALL021901_D1"
[1] "Post QC Cells: n = 4941"
Time difference of 2.19486 mins
[1] "Mapping: SJBALL021947_D1"
[1] "Post QC Cells: n = 5474"
Time difference of 2.438453 mins
[1] "Mapping: SJBALL021964_D1"
[1] "Post QC Cells: n = 5948"
Time difference of 2.73578 mins
[1] "Mapping: SJBALL021968_D1"
[1] "Post QC Cells: n = 6391"
Time difference of 3.032955 mins
[1] "Mapping: SJBALL021973_D1"
[1] "Post QC Cells: n = 6915"
Time difference of 2.859825 mins
[1] "Mapping: SJBALL022020_D1"
[1] "Post QC Cells: n = 3168"
Time difference of 1.37409 mins
[1] "Mapping: SJBALL022035_D1"
[1] "Post QC Cells: n = 2575"
Time difference of 1.058198 mins
[1] "Mapping: SJBALL022052_D1"
[1] "Post QC Cells: n = 6603"
Time difference of 2.758308 mins
[1] "Mapping: SJBALL030036_D1"
[1] "Post QC Cells: n = 3522"
Time difference of 1.578058 mins
[1] "Mapping: SJBALL030040_D1"
[1] "Post QC Cells: n = 10583"
Time difference of 4.171142 mins
[1] "Mapping: SJBALL030059_D1"
[1] "Post QC Cells: n = 4161"
Time difference of 1.926097 mins
[1] "Mapping: SJBALL030072_D1"
[1] "Post QC Cells: n = 8199"
Time difference of 3.30103 mins
[1] "Mapping: SJBALL030090_D1"
[1] "Post QC Cells: n = 5667"
Time difference of 2.344833 mins
[1] "Mapping: SJBALL030095_D1"
[1] "Post QC Cells: n = 6436"
Time difference of 2.756326 mins
[1] "Mapping: SJBALL030123_D1"
[1] "Post QC Cells: n = 5296"
Time difference of 2.15834 mins
[1] "Mapping: SJBALL030123_R1"
[1] "Post QC Cells: n = 7778"
Time difference of 3.493651 mins
[1] "Mapping: SJBALL030127_D1"
[1] "Post QC Cells: n = 6308"
Time difference of 2.64283 mins
[1] "Mapping: SJBALL030145_D1"
[1] "Post QC Cells: n = 5614"
Time difference of 2.476466 mins
[1] "Mapping: SJBALL030216_D1"
[1] "Post QC Cells: n = 7066"
Time difference of 3.031766 mins
[1] "Mapping: SJBALL030247_D1"
[1] "Post QC Cells: n = 6647"
Time difference of 2.890504 mins
[1] "Mapping: SJBALL030254_D1"
[1] "Post QC Cells: n = 6451"
Time difference of 2.824079 mins
[1] "Mapping: SJBALL030276_D1"
[1] "Post QC Cells: n = 5268"
Time difference of 2.464801 mins
[1] "Mapping: SJBALL030285_D1"
[1] "Post QC Cells: n = 7991"
Time difference of 3.867738 mins
[1] "Mapping: SJBALL030313_D1"
[1] "Post QC Cells: n = 7466"
Time difference of 3.088398 mins
[1] "Mapping: SJBALL030344_D1"
[1] "Post QC Cells: n = 7453"
Time difference of 3.17111 mins
[1] "Mapping: SJBALL030370_D1"
[1] "Post QC Cells: n = 5952"
Time difference of 2.771318 mins
[1] "Mapping: SJBALL030379_D1"
[1] "Post QC Cells: n = 5189"
Time difference of 2.124042 mins
[1] "Mapping: SJBALL030414_D1"
[1] "Post QC Cells: n = 4100"
Time difference of 1.736319 mins
[1] "Mapping: SJBALL030434_D1"
[1] "Post QC Cells: n = 1165"
Time difference of 33.57605 secs
[1] "Mapping: SJBALL030491_D1"
[1] "Post QC Cells: n = 6936"
Time difference of 2.941001 mins
[1] "Mapping: SJBALL030662_D1"
[1] "Post QC Cells: n = 6615"
Time difference of 2.813917 mins
[1] "Mapping: SJBALL030718_D1"
[1] "Post QC Cells: n = 7674"
Time difference of 3.267861 mins
[1] "Mapping: SJBALL030734_D1"
[1] "Post QC Cells: n = 8075"
Time difference of 3.471585 mins
[1] "Mapping: SJBALL030762_D1"
[1] "Post QC Cells: n = 4836"
Time difference of 2.008734 mins
[1] "Mapping: SJBALL030821_D1"
[1] "Post QC Cells: n = 2478"
Time difference of 1.044536 mins
[1] "Mapping: SJBALL030871_D1"
[1] "Post QC Cells: n = 5767"
Time difference of 2.340077 mins
[1] "Mapping: SJBALL030923_D1"
[1] "Post QC Cells: n = 6031"
Time difference of 2.555967 mins
[1] "Mapping: SJBALL030971_D1"
[1] "Post QC Cells: n = 6106"
Time difference of 2.603852 mins
[1] "Mapping: SJBALL030975_D1"
[1] "Post QC Cells: n = 7253"
Time difference of 3.045372 mins
[1] "Mapping: SJBALL031052_D1"
[1] "Post QC Cells: n = 5253"
Time difference of 2.107476 mins
[1] "Mapping: SJBALL031087_D1"
[1] "Post QC Cells: n = 4536"
Time difference of 1.793173 mins
[1] "Mapping: SJBALL031128_D1"
[1] "Post QC Cells: n = 8008"
Time difference of 3.273764 mins
[1] "Mapping: SJBALL031144_D1"
[1] "Post QC Cells: n = 7299"
Time difference of 2.859784 mins
[1] "Mapping: SJBALL031168_D1"
[1] "Post QC Cells: n = 6672"
Time difference of 2.69549 mins
[1] "Mapping: SJBALL031267_D1"
[1] "Post QC Cells: n = 5382"
Time difference of 2.050455 mins
[1] "Mapping: SJBALL031281_D1"
[1] "Post QC Cells: n = 5787"
Time difference of 2.220514 mins
[1] "Mapping: SJBALL081_D"
[1] "Post QC Cells: n = 8414"
Time difference of 3.501642 mins
[1] "Mapping: SJBALL087_D"
[1] "Post QC Cells: n = 7239"
Time difference of 2.984561 mins
[1] "Mapping: SJBALL104_D"
[1] "Post QC Cells: n = 6935"
Time difference of 2.87639 mins
[1] "Mapping: SJBALL113_D"
[1] "Post QC Cells: n = 5761"
Time difference of 2.378711 mins
[1] "Mapping: SJBALL182_D"
[1] "Post QC Cells: n = 5120"
Time difference of 3.214769 mins
[1] "Mapping: SJBALL182_R"
[1] "Post QC Cells: n = 7803"
Time difference of 3.481382 mins
[1] "Mapping: SJBALL211_D"
[1] "Post QC Cells: n = 4775"
Time difference of 2.162919 mins
[1] "Mapping: SJBALL243_D"
[1] "Post QC Cells: n = 5868"
Time difference of 2.538521 mins
[1] "Mapping: SJBALL255_D"
[1] "Post QC Cells: n = 6194"
Time difference of 2.721762 mins
[1] "Mapping: SJE2A063_D"
[1] "Post QC Cells: n = 6750"
Time difference of 2.643834 mins
[1] "Mapping: SJE2A066_D"
[1] "Post QC Cells: n = 7933"
Time difference of 3.38001 mins
[1] "Mapping: SJE2A067_D"
[1] "Post QC Cells: n = 3296"
Time difference of 1.357974 mins
[1] "Mapping: SJHYPER022017_D1"
[1] "Post QC Cells: n = 9613"
Time difference of 3.90506 mins
[1] "Mapping: SJHYPO021982_D1"
[1] "Post QC Cells: n = 9330"
Time difference of 3.718171 mins
[1] "Mapping: SJHYPO117_D"
[1] "Post QC Cells: n = 6565"
Time difference of 2.583308 mins
[1] "Mapping: SJHYPO120_D"
[1] "Post QC Cells: n = 6420"
Time difference of 2.829995 mins
[1] "Mapping: SJHYPO124_D"
[1] "Post QC Cells: n = 3810"
Time difference of 1.702335 mins
[1] "Mapping: SJHYPO143_D"
[1] "Post QC Cells: n = 2121"
Time difference of 1.297093 mins
[1] "Mapping: SJHYPO146_D"
[1] "Post QC Cells: n = 4816"
Time difference of 1.95948 mins
[1] "Mapping: SJINF022043_D"
[1] "Post QC Cells: n = 5772"
Time difference of 2.324233 mins
[1] "Mapping: SJMLL006_D"
[1] "Post QC Cells: n = 4585"
Time difference of 1.96375 mins
[1] "Mapping: SJMLL009_D"
[1] "Post QC Cells: n = 7172"
Time difference of 3.184118 mins
[1] "Mapping: SJPHALL004_D"
[1] "Post QC Cells: n = 6069"
Time difference of 2.494174 mins
[1] "Mapping: SJPHALL006_D"
[1] "Post QC Cells: n = 5805"
Time difference of 2.316203 mins
[1] "Mapping: SJPHALL007_D"
[1] "Post QC Cells: n = 7460"
Time difference of 3.054827 mins
[1] "Mapping: SJPHALL010_D"
[1] "Post QC Cells: n = 3136"
Time difference of 1.44603 mins
[1] "Mapping: SJPHALL020_D"
[1] "Post QC Cells: n = 8538"
Time difference of 3.60041 mins
[1] "Mapping: SJPHALL020_R"
[1] "Post QC Cells: n = 7582"
Time difference of 3.332086 mins
[1] "Mapping: SJPHALL021_D"
[1] "Post QC Cells: n = 6323"
Time difference of 2.660585 mins
BALL_composition_BMref %>% write_csv('BALL_projections/Complete_BoneMarrowReference/BALL_celltype_annotations_FullReference.csv')

Project onto B Cell Development

map_sample_BDev <- function(seurat_obj, batch_key){
  # Map cells
  seurat_obj <- mapQuery(
      seurat_obj@assays$SymphonyQuery@counts, 
      seurat_obj@meta.data,
      Bdev_ref,
      vars = batch_key, 
      return_type = 'Seurat'
  ) 
  
  # Predict Labels 
  seurat_obj <- knnPredict.Seurat(
    seurat_obj, 
    Bdev_ref, 
    label_transfer = 'BDevelopment_CellType_Comprehensive', 
    k = 30
  ) 
  return(seurat_obj)
}
library(ggpointdensity)
library(jcolors)

plot_BDev_projection <- function(dat, ptx_id, refUMAP, downsample_reference = TRUE, save_folder = 'BALL_projections/Focused_BDevelopment/Figures/'){
  
  if(downsample_reference){
    set.seed(123)
    refUMAP <- refUMAP %>% sample_frac(0.5)
  }
  
  dat <- dat@meta.data %>% rownames_to_column('Cell') %>% 
    left_join(dat@reductions$umap@cell.embeddings %>% data.frame() %>% rownames_to_column('Cell') ) %>% 
    filter(mapping_error_QC == 'Pass') %>% 
    mutate(ref_query = 'query') %>% 
    bind_rows(refUMAP)
  
  ## Get the Background
  background <-  dat %>% filter(ref_query == 'reference') %>% select(-Sample)
  heatpalette <- heat.colors(12)
  
  p <- dat %>% 
    filter(ref_query == 'query') %>% 
    ggplot(aes(x = -umap_1, y = umap_2)) +  
    geom_point(data = background, color='#E3E3E3', size=0.005, alpha=0.5) + 
    geom_pointdensity(size=0.02) +
    scale_color_jcolors_contin("pal3", reverse = TRUE, bias = 1.75) +
    geom_density_2d(alpha=0.4, color='black', h = 1.5, size=0.3) + 
    theme_void() + ggtitle(ptx_id) + 
    theme(strip.text.x = element_text(size=18), legend.position='none') 
  
  ggsave(paste0(save_folder, ptx_id, '_BdevelopmentReference_projectedUMAP.pdf'), height = 4, width = 6)
}
get_UMAPcoordinates <- function(BALL, umap_coordinates){
  # get composition per cell
  sample_coordinates <- BALL@meta.data %>% rownames_to_column('barcode') %>%
    select(barcode, Sample) %>% 
    left_join(BALL@reductions$umap@cell.embeddings %>% data.frame() %>% rownames_to_column('barcode') )
  
  # concatenate 
  umap_coordinates <- bind_rows(umap_coordinates, sample_coordinates)
  return(umap_coordinates)
}
get_composition_Bdev <- function(BALL, composition_Bdev){
  # get composition per cell
  sample_composition <- BALL@meta.data %>% rownames_to_column('barcode') %>%
    select(barcode, Sample, BDevelopment_CellType_Comprehensive, BDevelopment_CellType_Comprehensive_prob)
  
  # concatenate 
  composition_Bdev <- bind_rows(composition_Bdev, sample_composition)
  return(composition_Bdev)
}

Approach

For each projected sample we will subset the following celltypes: - HSC, MPP-LMPP, LMPP, Early GMP, MLP, MLP-II - Pre-pDC, Pre-pDC Cycling, pDC - CLP, Pre-ProB, Pro-B VDJ, Pro-B Cycling, Large Pre-B, Small Pre-B, Immature B, Mature B

BDev_celltypes <- c('HSC', 'HSC/MPP', 'MPP-MyLy', 'MPP-LMPP', 'LMPP', 'Early GMP', 'MLP', 'MLP-II', 'Pre-pDC', 'Pre-pDC Cycling', 'pDC', 
                    'CLP', 'EarlyProB', 'Pre-ProB', 'Pro-B VDJ', 'Pro-B Cycling', 'Large Pre-B', 'Small Pre-B', 'Immature B', 'Mature B')
projectedpath = 'BALL_projections/Complete_BoneMarrowReference/'
#matrixpath = '/filtered_feature_bc_matrix/'
BALL_patients_projected <- list.files(projectedpath, pattern = 'rds')
BALL_patients_projected
 [1] "SJALL040053_D1_projected.rds"   "SJALL040066_D1_projected.rds"   "SJALL040069_D1_projected.rds"   "SJALL040070_D1_projected.rds"  
 [5] "SJALL040099_D1_projected.rds"   "SJALL040100_D1_projected.rds"   "SJALL040103_D1_projected.rds"   "SJALL040119_D1_projected.rds"  
 [9] "SJALL040137_D1_projected.rds"   "SJALL040137_projected.rds"      "SJBALL004097_D2_projected.rds"  "SJBALL006_D_projected.rds"     
[13] "SJBALL006_R_projected.rds"      "SJBALL014876_D1_projected.rds"  "SJBALL021901_D1_projected.rds"  "SJBALL021947_D1_projected.rds" 
[17] "SJBALL021964_D1_projected.rds"  "SJBALL021968_D1_projected.rds"  "SJBALL021973_D1_projected.rds"  "SJBALL022020_D1_projected.rds" 
[21] "SJBALL022035_D1_projected.rds"  "SJBALL022052_D1_projected.rds"  "SJBALL030036_D1_projected.rds"  "SJBALL030040_D1_projected.rds" 
[25] "SJBALL030059_D1_projected.rds"  "SJBALL030072_D1_projected.rds"  "SJBALL030090_D1_projected.rds"  "SJBALL030095_D1_projected.rds" 
[29] "SJBALL030123_D1_projected.rds"  "SJBALL030123_R1_projected.rds"  "SJBALL030127_D1_projected.rds"  "SJBALL030145_D1_projected.rds" 
[33] "SJBALL030216_D1_projected.rds"  "SJBALL030247_D1_projected.rds"  "SJBALL030254_D1_projected.rds"  "SJBALL030276_D1_projected.rds" 
[37] "SJBALL030285_D1_projected.rds"  "SJBALL030313_D1_projected.rds"  "SJBALL030344_D1_projected.rds"  "SJBALL030370_D1_projected.rds" 
[41] "SJBALL030379_D1_projected.rds"  "SJBALL030414_D1_projected.rds"  "SJBALL030434_D1_projected.rds"  "SJBALL030491_D1_projected.rds" 
[45] "SJBALL030662_D1_projected.rds"  "SJBALL030718_D1_projected.rds"  "SJBALL030734_D1_projected.rds"  "SJBALL030762_D1_projected.rds" 
[49] "SJBALL030821_D1_projected.rds"  "SJBALL030871_D1_projected.rds"  "SJBALL030923_D1_projected.rds"  "SJBALL030971_D1_projected.rds" 
[53] "SJBALL030975_D1_projected.rds"  "SJBALL031052_D1_projected.rds"  "SJBALL031087_D1_projected.rds"  "SJBALL031128_D1_projected.rds" 
[57] "SJBALL031144_D1_projected.rds"  "SJBALL031168_D1_projected.rds"  "SJBALL031267_D1_projected.rds"  "SJBALL031281_D1_projected.rds" 
[61] "SJBALL081_D_projected.rds"      "SJBALL087_D_projected.rds"      "SJBALL104_D_projected.rds"      "SJBALL113_D_projected.rds"     
[65] "SJBALL182_D_projected.rds"      "SJBALL182_R_projected.rds"      "SJBALL211_D_projected.rds"      "SJBALL243_D_projected.rds"     
[69] "SJBALL255_D_projected.rds"      "SJE2A063_D_projected.rds"       "SJE2A066_D_projected.rds"       "SJE2A067_D_projected.rds"      
[73] "SJHYPER022017_D1_projected.rds" "SJHYPO021982_D1_projected.rds"  "SJHYPO117_D_projected.rds"      "SJHYPO120_D_projected.rds"     
[77] "SJHYPO124_D_projected.rds"      "SJHYPO143_D_projected.rds"      "SJHYPO146_D_projected.rds"      "SJINF022043_D_projected.rds"   
[81] "SJMLL006_D_projected.rds"       "SJMLL009_D_projected.rds"       "SJPHALL004_D_projected.rds"     "SJPHALL006_D_projected.rds"    
[85] "SJPHALL007_D_projected.rds"     "SJPHALL010_D_projected.rds"     "SJPHALL020_D_projected.rds"     "SJPHALL020_R_projected.rds"    
[89] "SJPHALL021_D_projected.rds"    
BALL_patients_projected[1]
[1] "SJALL040053_D1_projected.rds"
BALL <- readRDS(paste0(projectedpath, BALL_patients_projected[1])) 
BALL
An object of class Seurat 
36601 features across 4088 samples within 1 assay 
Active assay: SymphonyQuery (36601 features, 0 variable features)
 3 dimensional reductions calculated: pca, harmony, umap
BALL_UMAPcoordinates_BMref <- data.frame()
BALL_UMAPcoordinates_BMref <- get_UMAPcoordinates(BALL, BALL_UMAPcoordinates_BMref)
Joining, by = "barcode"
BALL_UMAPcoordinates_BMref

MAKE SURE TO RECORD UMAP COORDINATES from normal and from BDev

refUMAP_bdev <- data.frame(Bdev_ref$umap$embedding) %>% rename(umap_1 = X1, umap_2 = X2) %>% mutate(ref_query = 'reference')
BALL_composition_BDev <- data.frame()
BALL_UMAPcoordinates_BMref <- data.frame()
BALL_UMAPcoordinates_BDev <- data.frame()

# Test the first sample
for(pt_samp in BALL_patients_projected){
  print(paste0('Mapping: ', pt_samp))
  start_time <- Sys.time()
  # load in as seurat obj
  BALL <- readRDS(paste0(projectedpath, pt_samp)) 
  # get UMAP coordinates from BM Reference mapping
  BALL_UMAPcoordinates_BMref <- get_UMAPcoordinates(BALL, BALL_UMAPcoordinates_BMref)
  # subset to include B development lineage
  BALL <- subset(BALL, CellType_Annotation %in% BDev_celltypes)
  # Map sample onto reference map and label with 30 KNN, no batch correction
  pt_samp <- pt_samp %>% str_replace('_projected.rds','')
  BALL <- map_sample_BDev(BALL, NULL)
  # plot projected cells onto UMAP 
  plot_BDev_projection(BALL, pt_samp, refUMAP = refUMAP_bdev, downsample_reference = TRUE, save_folder = 'BALL_projections/Focused_BDevelopment/Figures/')
  # get celltype composition
  BALL_composition_BDev <- get_composition_Bdev(BALL, BALL_composition_BDev)
  # get UMAP coordinates from BDevelopment mapping 
  BALL_UMAPcoordinates_BDev <- get_UMAPcoordinates(BALL, BALL_UMAPcoordinates_BDev)
  # save
  BALL %>% saveRDS(paste0("BALL_projections/Focused_BDevelopment/", pt_samp, "_projected_BDevFocus.rds"))
  rm(BALL)
  
  end_time <- Sys.time()
  print(end_time - start_time)
  gc()
}
[1] "Mapping: SJALL040053_D1_projected.rds"
Joining, by = "barcode"Normalizing
Scaling and synchronizing query gene expression
Found 950 reference variable genes in query dataset
Project query cells using reference gene loadings
Clustering query cells to reference centroids
Correcting query batch effects
UMAP
All done!
Warning: Invalid name supplied, making object name syntactically valid. New object name is Seurat..ProjectDim.SymphonyQuery.harmony; see ?make.names for more details on syntax validityJoining, by = "Cell"
Time difference of 50.42848 secs
[1] "Mapping: SJALL040066_D1_projected.rds"
Time difference of 1.430251 mins
[1] "Mapping: SJALL040069_D1_projected.rds"
Time difference of 1.463659 mins
[1] "Mapping: SJALL040070_D1_projected.rds"
Time difference of 48.96805 secs
[1] "Mapping: SJALL040099_D1_projected.rds"
Time difference of 1.069611 mins
[1] "Mapping: SJALL040100_D1_projected.rds"
Time difference of 1.398232 mins
[1] "Mapping: SJALL040103_D1_projected.rds"
Time difference of 56.95845 secs
[1] "Mapping: SJALL040119_D1_projected.rds"
Time difference of 2.135658 mins
[1] "Mapping: SJALL040137_D1_projected.rds"
Time difference of 1.293413 mins
[1] "Mapping: SJALL040137_projected.rds"
Time difference of 1.134639 mins
[1] "Mapping: SJBALL004097_D2_projected.rds"
Time difference of 33.78636 secs
[1] "Mapping: SJBALL006_D_projected.rds"
Time difference of 1.677079 mins
[1] "Mapping: SJBALL006_R_projected.rds"
Time difference of 59.21684 secs
[1] "Mapping: SJBALL014876_D1_projected.rds"
Time difference of 23.18006 secs
[1] "Mapping: SJBALL021901_D1_projected.rds"
Time difference of 1.042573 mins
[1] "Mapping: SJBALL021947_D1_projected.rds"
Time difference of 50.92988 secs
[1] "Mapping: SJBALL021964_D1_projected.rds"
Time difference of 1.228102 mins
[1] "Mapping: SJBALL021968_D1_projected.rds"
Time difference of 1.3662 mins
[1] "Mapping: SJBALL021973_D1_projected.rds"
Time difference of 1.486559 mins
[1] "Mapping: SJBALL022020_D1_projected.rds"
Time difference of 40.7226 secs
[1] "Mapping: SJBALL022035_D1_projected.rds"
Time difference of 32.62492 secs
[1] "Mapping: SJBALL022052_D1_projected.rds"
Time difference of 1.361965 mins
[1] "Mapping: SJBALL030036_D1_projected.rds"
Time difference of 36.27694 secs
[1] "Mapping: SJBALL030040_D1_projected.rds"
Time difference of 2.002704 mins
[1] "Mapping: SJBALL030059_D1_projected.rds"
Time difference of 46.49931 secs
[1] "Mapping: SJBALL030072_D1_projected.rds"
Time difference of 1.35629 mins
[1] "Mapping: SJBALL030090_D1_projected.rds"
Time difference of 1.083832 mins
[1] "Mapping: SJBALL030095_D1_projected.rds"
Time difference of 1.236406 mins
[1] "Mapping: SJBALL030123_D1_projected.rds"
Time difference of 54.29487 secs
[1] "Mapping: SJBALL030123_R1_projected.rds"
Time difference of 1.401866 mins
[1] "Mapping: SJBALL030127_D1_projected.rds"
Time difference of 1.179053 mins
[1] "Mapping: SJBALL030145_D1_projected.rds"
Time difference of 1.150743 mins
[1] "Mapping: SJBALL030216_D1_projected.rds"
Time difference of 1.328065 mins
[1] "Mapping: SJBALL030247_D1_projected.rds"
Time difference of 1.455484 mins
[1] "Mapping: SJBALL030254_D1_projected.rds"
Time difference of 1.091389 mins
[1] "Mapping: SJBALL030276_D1_projected.rds"
Time difference of 1.152758 mins
[1] "Mapping: SJBALL030285_D1_projected.rds"
Time difference of 1.828319 mins
[1] "Mapping: SJBALL030313_D1_projected.rds"
Time difference of 1.375607 mins
[1] "Mapping: SJBALL030344_D1_projected.rds"
Time difference of 1.470846 mins
[1] "Mapping: SJBALL030370_D1_projected.rds"
Time difference of 1.280466 mins
[1] "Mapping: SJBALL030379_D1_projected.rds"
Time difference of 1.024783 mins
[1] "Mapping: SJBALL030414_D1_projected.rds"
Time difference of 39.38767 secs
[1] "Mapping: SJBALL030434_D1_projected.rds"
Time difference of 9.560939 secs
[1] "Mapping: SJBALL030491_D1_projected.rds"
Time difference of 1.428839 mins
[1] "Mapping: SJBALL030662_D1_projected.rds"
Time difference of 1.326386 mins
[1] "Mapping: SJBALL030718_D1_projected.rds"
Time difference of 1.444446 mins
[1] "Mapping: SJBALL030734_D1_projected.rds"
Time difference of 1.640176 mins
[1] "Mapping: SJBALL030762_D1_projected.rds"
Time difference of 58.13085 secs
[1] "Mapping: SJBALL030821_D1_projected.rds"
Time difference of 33.09449 secs
[1] "Mapping: SJBALL030871_D1_projected.rds"
Time difference of 49.77356 secs
[1] "Mapping: SJBALL030923_D1_projected.rds"
Time difference of 1.174341 mins
[1] "Mapping: SJBALL030971_D1_projected.rds"
Time difference of 1.075251 mins
[1] "Mapping: SJBALL030975_D1_projected.rds"
Time difference of 1.405576 mins
[1] "Mapping: SJBALL031052_D1_projected.rds"
Time difference of 1.013604 mins
[1] "Mapping: SJBALL031087_D1_projected.rds"
Time difference of 55.91589 secs
[1] "Mapping: SJBALL031128_D1_projected.rds"
Time difference of 1.540495 mins
[1] "Mapping: SJBALL031144_D1_projected.rds"
Time difference of 1.110498 mins
[1] "Mapping: SJBALL031168_D1_projected.rds"
Time difference of 1.442441 mins
[1] "Mapping: SJBALL031267_D1_projected.rds"
Time difference of 1.035721 mins
[1] "Mapping: SJBALL031281_D1_projected.rds"
Time difference of 1.137457 mins
[1] "Mapping: SJBALL081_D_projected.rds"
Time difference of 1.930735 mins
[1] "Mapping: SJBALL087_D_projected.rds"
Time difference of 1.210253 mins
[1] "Mapping: SJBALL104_D_projected.rds"
Time difference of 1.440626 mins
[1] "Mapping: SJBALL113_D_projected.rds"
Time difference of 1.132059 mins
[1] "Mapping: SJBALL182_D_projected.rds"
Time difference of 1.032651 mins
[1] "Mapping: SJBALL182_R_projected.rds"
Time difference of 49.34748 secs
[1] "Mapping: SJBALL211_D_projected.rds"
Time difference of 1.015188 mins
[1] "Mapping: SJBALL243_D_projected.rds"
Time difference of 1.21827 mins
[1] "Mapping: SJBALL255_D_projected.rds"
Time difference of 1.164521 mins
[1] "Mapping: SJE2A063_D_projected.rds"
Time difference of 1.388237 mins
[1] "Mapping: SJE2A066_D_projected.rds"
Time difference of 1.734002 mins
[1] "Mapping: SJE2A067_D_projected.rds"
Time difference of 37.33951 secs
[1] "Mapping: SJHYPER022017_D1_projected.rds"
Time difference of 2.039134 mins
[1] "Mapping: SJHYPO021982_D1_projected.rds"
Time difference of 1.872619 mins
[1] "Mapping: SJHYPO117_D_projected.rds"
Time difference of 1.211332 mins
[1] "Mapping: SJHYPO120_D_projected.rds"
Time difference of 1.450417 mins
[1] "Mapping: SJHYPO124_D_projected.rds"
Time difference of 39.14564 secs
[1] "Mapping: SJHYPO143_D_projected.rds"
Time difference of 25.34178 secs
[1] "Mapping: SJHYPO146_D_projected.rds"
Time difference of 41.4173 secs
[1] "Mapping: SJINF022043_D_projected.rds"
Time difference of 1.129802 mins
[1] "Mapping: SJMLL006_D_projected.rds"
Time difference of 1.017289 mins
[1] "Mapping: SJMLL009_D_projected.rds"
Time difference of 1.613155 mins
[1] "Mapping: SJPHALL004_D_projected.rds"
Time difference of 1.20073 mins
[1] "Mapping: SJPHALL006_D_projected.rds"
Time difference of 1.010509 mins
[1] "Mapping: SJPHALL007_D_projected.rds"
Time difference of 1.488113 mins
[1] "Mapping: SJPHALL010_D_projected.rds"
Time difference of 34.97153 secs
[1] "Mapping: SJPHALL020_D_projected.rds"
Time difference of 1.885798 mins
[1] "Mapping: SJPHALL020_R_projected.rds"
Time difference of 1.646972 mins
[1] "Mapping: SJPHALL021_D_projected.rds"
Time difference of 1.869184 mins
# save cell annotations
BALL_composition_BDev %>% write_csv('BALL_projections/Focused_BDevelopment/BALL_celltype_annotations_Bdevelopment.csv')
# save UMAP coordinates for FullReference and BDevelopment
BALL_UMAPcoordinates_BMref %>% write_csv('BALL_projections/Complete_BoneMarrowReference/BALL_UMAPcoordinates_FullReference.csv')
BALL_UMAPcoordinates_BDev %>% write_csv('BALL_projections/Focused_BDevelopment/BALL_UMAPcoordinates_Bdevelopment.csv')
BALL_UMAPcoordinates_BMref %>% write_csv('BALL_projections/Complete_BoneMarrowReference/BALL_UMAPcoordinates_FullReference.csv')
BALL_UMAPcoordinates_BDev %>% write_csv('BALL_projections/Focused_BDevelopment/BALL_UMAPcoordinates_Bdevelopment.csv')

Get Composition

BALL_UMAPcoordinates_BMref %>% dplyr::rename(FullReference_UMAP1 = umap_1, FullReference_UMAP2 = umap_2)
BALL_UMAPcoordinates_BDev %>% mutate(umap_1 = -umap_1) %>% dplyr::rename(BDevelopment_UMAP1 = umap_1, BDevelopment_UMAP2 = umap_2)
BALL_composition %>% pull(Directory) %>% table() %>% sort()
.
 SJBALL030434_D1  SJBALL014876_D1      SJHYPO143_D  SJBALL030821_D1  SJBALL022035_D1  SJBALL004097_D2     SJPHALL010_D  SJBALL022020_D1       SJE2A067_D 
            1165             1729             2121             2478             2575             2978             3136             3168             3296 
 SJBALL030036_D1      SJHYPO124_D   SJALL040053_D1  SJBALL030414_D1  SJBALL030059_D1   SJALL040070_D1  SJBALL031087_D1       SJMLL006_D      SJBALL006_R 
            3522             3810             4088             4100             4161             4486             4536             4585             4738 
     SJBALL211_D      SJHYPO146_D  SJBALL030762_D1   SJALL040103_D1  SJBALL021901_D1      SJBALL182_D  SJBALL030379_D1  SJBALL031052_D1  SJBALL030276_D1 
            4775             4816             4836             4853             4941             5120             5189             5253             5268 
 SJBALL030123_D1  SJBALL031267_D1   SJALL040099_D1  SJBALL021947_D1  SJBALL030145_D1  SJBALL030090_D1      SJBALL113_D  SJBALL030871_D1    SJINF022043_D 
            5296             5382             5403             5474             5614             5667             5761             5767             5772 
 SJBALL031281_D1     SJPHALL006_D      SJBALL243_D  SJBALL021964_D1  SJBALL030370_D1  SJBALL030923_D1     SJPHALL004_D  SJBALL030971_D1      SJBALL255_D 
            5787             5805             5868             5948             5952             6031             6069             6106             6194 
     SJALL040137  SJBALL030127_D1     SJPHALL021_D  SJBALL021968_D1      SJHYPO120_D  SJBALL030095_D1  SJBALL030254_D1      SJHYPO117_D  SJBALL022052_D1 
            6202             6308             6323             6391             6420             6436             6451             6565             6603 
 SJBALL030662_D1  SJBALL030247_D1  SJBALL031168_D1   SJALL040137_D1       SJE2A063_D   SJALL040066_D1   SJALL040100_D1  SJBALL021973_D1      SJBALL104_D 
            6615             6647             6672             6737             6750             6880             6897             6915             6935 
 SJBALL030491_D1  SJBALL030216_D1       SJMLL009_D      SJBALL087_D  SJBALL030975_D1  SJBALL031144_D1  SJBALL030344_D1     SJPHALL007_D  SJBALL030313_D1 
            6936             7066             7172             7239             7253             7299             7453             7460             7466 
    SJPHALL020_R      SJBALL006_D  SJBALL030718_D1  SJBALL030123_R1      SJBALL182_R       SJE2A066_D  SJBALL030285_D1  SJBALL031128_D1   SJALL040069_D1 
            7582             7649             7674             7778             7803             7933             7991             8008             8056 
 SJBALL030734_D1  SJBALL030072_D1      SJBALL081_D     SJPHALL020_D   SJALL040119_D1  SJHYPO021982_D1 SJHYPER022017_D1  SJBALL030040_D1 
            8075             8199             8414             8538             9313             9330             9613            10583 

Methods

Raw counts from filtered BALL cells were projected onto the bone marrow reference map using Symphony (Kang et al). Filtering criteria was: - % mito < 8 - nCount_RNA > 2500 - nFeature_RNA > 500

Cells assigned to be within the B-cell development lineage: - HSC, MPP-LMPP, LMPP, Early GMP, MLP, MLP-II - CLP, Pre-ProB, Pro-B VDJ, Pro-B Cycling, Large Pre-B, Small Pre-B, Immature B, Mature B - Pre-pDC, Pre-pDC Cycling, pDC

Were subsetted and projected onto the cross-ontogeny map of B-cell development using Symphony to refine cell type classification along the B cell lineage.

---
title: "B-ALL Rapid Projections"
output: html_notebook
---

Rapid projections for B-ALL data

```{r}
library(Seurat)
library(symphony)
library(tidyverse)
```

**Bone Marrow Reference Map**

```{r}
BM_projection_path = '../../../../../../AMLhierarchies/scRNA_projection/Complete_Hematopoiesis/BM_V2only/Final_Embedding/Final_projections/ProjectionTools/'
source(paste0(BM_projection_path, 'Symphony_Utils_BMref.R'))
BM_ref <- readRDS(paste0(BM_projection_path, 'BoneMarrow_RefMap_SymphonyRef.rds'))

# Fix uwot path for UMAP projection - point it to the directory with Projection Tools
BM_ref$save_uwot_path <- paste0(BM_projection_path, BM_ref$save_uwot_path)
```

```{r, fig.height = 4.5, fig.width = 10}
ReferenceSeuratObj_BM <- CreateReferenceSeuratObj(BM_ref)
DimPlot(ReferenceSeuratObj_BM, reduction = 'umap', group.by = 'CellType_Annotation_formatted', raster=FALSE, label=TRUE, label.size = 4)
```

**Cross-Ontogeny B-Development Map**

```{r}
Bdev_projection_path = 'BDevelopment_ProjectionTools/'
Bdev_ref <- readRDS(paste0(Bdev_projection_path, 'BDevelopment_RefMap_SymphonyRef.rds'))

# Fix uwot path for UMAP projection - point it to the directory with Projection Tools
Bdev_ref$save_uwot_path <- paste0(Bdev_projection_path, Bdev_ref$save_uwot_path)
```

```{r, fig.height = 5, fig.width = 11}
ReferenceSeuratObj_Bdev <- CreateReferenceSeuratObj(Bdev_ref)
# flip UMAP1 axis
ReferenceSeuratObj_Bdev[['umap']]@cell.embeddings[,1] = -ReferenceSeuratObj_Bdev[['umap']]@cell.embeddings[,1]
DimPlot(ReferenceSeuratObj_Bdev, reduction = 'umap', group.by = 'BDevelopment_CellType_Comprehensive', raster=FALSE, label=TRUE, label.size = 5)
```


### Run Samples

```{r}
library(ggpointdensity)
library(viridis)
library(jcolors)
```

```{r}
map_sample_BM <- function(seurat_obj, batch_key){
  
  # Map cells
  seurat_obj <- mapQuery(
      seurat_obj@assays$RNA@counts, 
      seurat_obj@meta.data,
      BM_ref,
      vars = batch_key, 
      return_type = 'Seurat'
  ) %>% calcMappingError(., reference = BM_ref, MAD.threshold = 2)
  
  # Predict Labels 
  seurat_obj <- knnPredict.Seurat(
    seurat_obj, 
    BM_ref, 
    label_transfer = 'CellType_Annotation', 
    k = 30
  ) 
  
  return(seurat_obj)
}
```

```{r}
library(ggpointdensity)
library(jcolors)

plot_projection <- function(dat, ptx_id, refUMAP = refUMAP, downsample_reference = TRUE, save_folder = 'BALL_projections/Complete_BoneMarrowReference/Figures/'){
  
  if(downsample_reference){
    refUMAP <- refUMAP %>% sample_frac(0.25)
  }
  
  dat <- dat@meta.data %>% rownames_to_column('Cell') %>% 
    left_join(dat@reductions$umap@cell.embeddings %>% data.frame() %>% rownames_to_column('Cell') ) %>% 
    filter(mapping_error_QC == 'Pass') %>% 
    mutate(ref_query = 'query') %>% 
    bind_rows(refUMAP)
  
  ## Get the Background
  background <-  dat %>% filter(ref_query == 'reference') %>% select(-Sample)
  heatpalette <- heat.colors(12)
  
  p <- dat %>% 
    filter(ref_query == 'query') %>% 
    ggplot(aes(x = umap_1, y = umap_2)) +  
    geom_point(data = background, color='#E3E3E3', size=0.01, alpha=0.5) + 
    geom_pointdensity(size=0.05) +
    scale_color_jcolors_contin("pal3", reverse = TRUE, bias = 1.75) +
    geom_density_2d(alpha=0.4, color='black', h = 1.5, size=0.4) + 
    theme_void() + ggtitle(ptx_id) + 
    theme(strip.text.x = element_text(size=18), legend.position='none') 
  
  ggsave(paste0(save_folder, ptx_id, '_BoneMarrowReference_projectedUMAP.pdf'), height = 4, width = 6)
}
```

```{r}
get_composition_BM <- function(BALL, composition_BM){
  # get composition per cell
  sample_composition <- BALL@meta.data %>% rownames_to_column('barcode') %>% 
    select(barcode, Sample, mapping_error_score, mapping_error_QC, 
           CellType_Annotation, CellType_Annotation_prob)
  
  # concatenate lol
  composition_BM <- bind_rows(composition_BM, sample_composition)
  return(composition_BM)
}
``` 


### Now lets iterate through samples and run

```{r}
basepath = 'BALL_rawdata/Expanded/'
matrixpath = '/filtered_feature_bc_matrix/'
BALL_patients <- list.files(basepath)
BALL_patients
```

```{r}
refUMAP <- data.frame(BM_ref$umap$embedding) %>% rename(umap_1 = X1, umap_2 = X2) %>% mutate(ref_query = 'reference')
BALL_composition_BMref <- data.frame()

# Test the first sample
for(pt_samp in BALL_patients){
  print(paste0('Mapping: ', pt_samp))
  start_time <- Sys.time()
  
  # load in as seurat obj
  BALL <- Seurat::Read10X(paste0(basepath, pt_samp, matrixpath)) %>% CreateSeuratObject()
  BALL$Sample <- pt_samp

  # QC >500 genes, >2500 counts, <8% mt counts
  BALL <- Seurat::PercentageFeatureSet(BALL, pattern = '^MT-', col.name = 'pct.mito')
  BALL <- subset(BALL, nFeature_RNA > 500 & nCount_RNA > 2500 & pct.mito < 8)
  print(paste0('Post QC Cells: n = ', dim(BALL)[2]))

  # Map sample onto reference map and label with 30 KNN, no batch correction
  BALL <- map_sample_BM(BALL, NULL)
  # plot projected cells onto UMAP 
  plot_projection(BALL, pt_samp, refUMAP = refUMAP, downsample_reference = TRUE, save_folder = 'BALL_projections/Complete_BoneMarrowReference/Figures/')
  # get celltype composition
  BALL_composition_BMref <- get_composition_BM(BALL, BALL_composition_BMref)
  # save
  BALL %>% saveRDS(paste0("BALL_projections/Complete_BoneMarrowReference/", pt_samp, "_projected.rds"))
  rm(BALL)
  
  end_time <- Sys.time()
  print(end_time - start_time)
  gc()
}

BALL_composition_BMref %>% write_csv('BALL_projections/Complete_BoneMarrowReference/BALL_celltype_annotations_FullReference.csv')
```

# Project onto B Cell Development 

```{r}
map_sample_BDev <- function(seurat_obj, batch_key){
  # Map cells
  seurat_obj <- mapQuery(
      seurat_obj@assays$SymphonyQuery@counts, 
      seurat_obj@meta.data,
      Bdev_ref,
      vars = batch_key, 
      return_type = 'Seurat'
  ) 
  
  # Predict Labels 
  seurat_obj <- knnPredict.Seurat(
    seurat_obj, 
    Bdev_ref, 
    label_transfer = 'BDevelopment_CellType_Comprehensive', 
    k = 30
  ) 
  return(seurat_obj)
}
```


```{r}
library(ggpointdensity)
library(jcolors)

plot_BDev_projection <- function(dat, ptx_id, refUMAP, downsample_reference = TRUE, save_folder = 'BALL_projections/Focused_BDevelopment/Figures/'){
  
  if(downsample_reference){
    set.seed(123)
    refUMAP <- refUMAP %>% sample_frac(0.5)
  }
  
  dat <- dat@meta.data %>% rownames_to_column('Cell') %>% 
    left_join(dat@reductions$umap@cell.embeddings %>% data.frame() %>% rownames_to_column('Cell') ) %>% 
    filter(mapping_error_QC == 'Pass') %>% 
    mutate(ref_query = 'query') %>% 
    bind_rows(refUMAP)
  
  ## Get the Background
  background <-  dat %>% filter(ref_query == 'reference') %>% select(-Sample)
  heatpalette <- heat.colors(12)
  
  p <- dat %>% 
    filter(ref_query == 'query') %>% 
    ggplot(aes(x = -umap_1, y = umap_2)) +  
    geom_point(data = background, color='#E3E3E3', size=0.005, alpha=0.5) + 
    geom_pointdensity(size=0.02) +
    scale_color_jcolors_contin("pal3", reverse = TRUE, bias = 1.75) +
    geom_density_2d(alpha=0.4, color='black', h = 1.5, size=0.3) + 
    theme_void() + ggtitle(ptx_id) + 
    theme(strip.text.x = element_text(size=18), legend.position='none') 
  
  ggsave(paste0(save_folder, ptx_id, '_BdevelopmentReference_projectedUMAP.pdf'), height = 4, width = 6)
}
```


```{r}
get_UMAPcoordinates <- function(BALL, umap_coordinates){
  # get composition per cell
  sample_coordinates <- BALL@meta.data %>% rownames_to_column('barcode') %>%
    select(barcode, Sample) %>% 
    left_join(BALL@reductions$umap@cell.embeddings %>% data.frame() %>% rownames_to_column('barcode') )
  
  # concatenate 
  umap_coordinates <- bind_rows(umap_coordinates, sample_coordinates)
  return(umap_coordinates)
}
``` 


```{r}
get_composition_Bdev <- function(BALL, composition_Bdev){
  # get composition per cell
  sample_composition <- BALL@meta.data %>% rownames_to_column('barcode') %>%
    select(barcode, Sample, BDevelopment_CellType_Comprehensive, BDevelopment_CellType_Comprehensive_prob)
  
  # concatenate 
  composition_Bdev <- bind_rows(composition_Bdev, sample_composition)
  return(composition_Bdev)
}
``` 



### Approach

For each projected sample we will subset the following celltypes: 
  - HSC, MPP-LMPP, LMPP, Early GMP, MLP, MLP-II
  - Pre-pDC, Pre-pDC Cycling, pDC
  - CLP, Pre-ProB, Pro-B VDJ, Pro-B Cycling, Large Pre-B, Small Pre-B, Immature B, Mature B
  
```{r}
BDev_celltypes <- c('HSC', 'HSC/MPP', 'MPP-MyLy', 'MPP-LMPP', 'LMPP', 'Early GMP', 'MLP', 'MLP-II', 'Pre-pDC', 'Pre-pDC Cycling', 'pDC', 
                    'CLP', 'EarlyProB', 'Pre-ProB', 'Pro-B VDJ', 'Pro-B Cycling', 'Large Pre-B', 'Small Pre-B', 'Immature B', 'Mature B')
```
  
```{r}
projectedpath = 'BALL_projections/Complete_BoneMarrowReference/'
#matrixpath = '/filtered_feature_bc_matrix/'
BALL_patients_projected <- list.files(projectedpath, pattern = 'rds')
BALL_patients_projected
```


```{r}
BALL_patients_projected[1]
BALL <- readRDS(paste0(projectedpath, BALL_patients_projected[1])) 
BALL
```

```{r}
BALL_UMAPcoordinates_BMref <- data.frame()
BALL_UMAPcoordinates_BMref <- get_UMAPcoordinates(BALL, BALL_UMAPcoordinates_BMref)
BALL_UMAPcoordinates_BMref
```

### MAKE SURE TO RECORD UMAP COORDINATES from normal and from BDev

```{r}
refUMAP_bdev <- data.frame(Bdev_ref$umap$embedding) %>% rename(umap_1 = X1, umap_2 = X2) %>% mutate(ref_query = 'reference')
BALL_composition_BDev <- data.frame()
BALL_UMAPcoordinates_BMref <- data.frame()
BALL_UMAPcoordinates_BDev <- data.frame()

# Test the first sample
for(pt_samp in BALL_patients_projected){
  print(paste0('Mapping: ', pt_samp))
  start_time <- Sys.time()
  # load in as seurat obj
  BALL <- readRDS(paste0(projectedpath, pt_samp)) 
  # get UMAP coordinates from BM Reference mapping
  BALL_UMAPcoordinates_BMref <- get_UMAPcoordinates(BALL, BALL_UMAPcoordinates_BMref)
  # subset to include B development lineage
  BALL <- subset(BALL, CellType_Annotation %in% BDev_celltypes)
  # Map sample onto reference map and label with 30 KNN, no batch correction
  pt_samp <- pt_samp %>% str_replace('_projected.rds','')
  BALL <- map_sample_BDev(BALL, NULL)
  # plot projected cells onto UMAP 
  plot_BDev_projection(BALL, pt_samp, refUMAP = refUMAP_bdev, downsample_reference = TRUE, save_folder = 'BALL_projections/Focused_BDevelopment/Figures/')
  # get celltype composition
  BALL_composition_BDev <- get_composition_Bdev(BALL, BALL_composition_BDev)
  # get UMAP coordinates from BDevelopment mapping 
  BALL_UMAPcoordinates_BDev <- get_UMAPcoordinates(BALL, BALL_UMAPcoordinates_BDev)
  # save
  BALL %>% saveRDS(paste0("BALL_projections/Focused_BDevelopment/", pt_samp, "_projected_BDevFocus.rds"))
  rm(BALL)
  
  end_time <- Sys.time()
  print(end_time - start_time)
  gc()
}
# save cell annotations
BALL_composition_BDev %>% write_csv('BALL_projections/Focused_BDevelopment/BALL_celltype_annotations_Bdevelopment.csv')
# save UMAP coordinates for FullReference and BDevelopment
BALL_UMAPcoordinates_BMref %>% write_csv('BALL_projections/Complete_BoneMarrowReference/BALL_UMAPcoordinates_FullReference.csv')
BALL_UMAPcoordinates_BDev %>% write_csv('BALL_projections/Focused_BDevelopment/BALL_UMAPcoordinates_Bdevelopment.csv')
```

```{r}
BALL_UMAPcoordinates_BMref %>% write_csv('BALL_projections/Complete_BoneMarrowReference/BALL_UMAPcoordinates_FullReference.csv')
BALL_UMAPcoordinates_BDev %>% write_csv('BALL_projections/Focused_BDevelopment/BALL_UMAPcoordinates_Bdevelopment.csv')
```




### Get Composition


```{r}
BALL_composition_BMref <- read_csv('BALL_projections/Complete_BoneMarrowReference/BALL_celltype_annotations_FullReference.csv')
BALL_composition_BDev <- read_csv('BALL_projections/Focused_BDevelopment/BALL_celltype_annotations_Bdevelopment.csv')
```

```{r}
BALL_UMAPcoordinates_BMref <- read_csv('BALL_projections/Complete_BoneMarrowReference/BALL_UMAPcoordinates_FullReference.csv')
BALL_UMAPcoordinates_BDev <- read_csv('BALL_projections/Focused_BDevelopment/BALL_UMAPcoordinates_Bdevelopment.csv')
```


```{r}
BALL_UMAPcoordinates_BMref %>% dplyr::rename(FullReference_UMAP1 = umap_1, FullReference_UMAP2 = umap_2)
BALL_UMAPcoordinates_BDev %>% mutate(umap_1 = -umap_1) %>% dplyr::rename(BDevelopment_UMAP1 = umap_1, BDevelopment_UMAP2 = umap_2)
```

```{r}
BALL_composition <- 
  BALL_composition_BMref %>% 
  left_join(BALL_composition_BDev) %>%
  left_join( BALL_UMAPcoordinates_BMref %>% dplyr::rename(UMAP1_FullReference = umap_1, UMAP2_FullReference = umap_2) ) %>% 
  left_join( BALL_UMAPcoordinates_BDev %>% mutate(umap_1 = -umap_1) %>% dplyr::rename(UMAP1_BDevelopment = umap_1, UMAP2_BDevelopment = umap_2) ) %>% 
  mutate(CellType_Annotation = CellType_Annotation %>% as.character(), 
         BDevelopment_CellType_Comprehensive = BDevelopment_CellType_Comprehensive %>% as.character()) %>%
  mutate(Final_CellType = ifelse(is.na(BDevelopment_CellType_Comprehensive), CellType_Annotation, BDevelopment_CellType_Comprehensive),
         Final_CellType_prob = ifelse(is.na(BDevelopment_CellType_Comprehensive_prob), CellType_Annotation_prob, BDevelopment_CellType_Comprehensive_prob)) %>%
  select(Cell = barcode, Directory = Sample, Final_CellType, Final_CellType_prob, everything()) 

BALL_composition %>% write_csv('BALL_projections/BALL_89pt_CellType_FinalAssignments.csv')
BALL_composition
```


```{r}
BALL_composition %>% pull(Directory) %>% table() %>% sort()
```


### Methods

Raw counts from filtered BALL cells were projected onto the bone marrow reference map using Symphony (Kang et al). 
Filtering criteria was: 
  - % mito < 8
  - nCount_RNA > 2500
  - nFeature_RNA > 500
  
Cells assigned to be within the B-cell development lineage: 
  - HSC, MPP-LMPP, LMPP, Early GMP, MLP, MLP-II
  - CLP, Pre-ProB, Pro-B VDJ, Pro-B Cycling, Large Pre-B, Small Pre-B, Immature B, Mature B
  - Pre-pDC, Pre-pDC Cycling, pDC

Were subsetted and projected onto the cross-ontogeny map of B-cell development using Symphony to refine cell type classification along the B cell lineage.
















